Publication Details
Title: Knowledge-Intensive Recruitment Learning
Author: J. Diederich
Group: ICSI Technical Reports
Date: November 1988
PDF: http://www.icsi.berkeley.edu/pubs/techreports/tr-88-010.pdf
Overview:
The model described in this paper is a knowledge-intensive connectionist learning system which uses a built-in knowledge representation module for inferencing, and this reasoning capability in turn is used for knowledge-intensive learning. On the connectionist network level, the central process is the recruitment of new units and the assembly of units to represent new conceptual information. Free, uncommitted subnetworks are connected to the built-in knowledge network during learning. The goal of knowledge-intensive connectionist learning is to improve the operationality of the knowledge representation: mediated inferences, i.e., complex inferences which require several inference steps, are transformed into immediate inferences; in other words, recognition is based on the immediate excitation from features directly associated with a concept.
Bibliographic Information:
ICSI Technical Report TR-88-010
Bibliographic Reference:
J. Diederich. Knowledge-Intensive Recruitment Learning. ICSI Technical Report TR-88-010, November 1988
Author: J. Diederich
Group: ICSI Technical Reports
Date: November 1988
PDF: http://www.icsi.berkeley.edu/pubs/techreports/tr-88-010.pdf
Overview:
The model described in this paper is a knowledge-intensive connectionist learning system which uses a built-in knowledge representation module for inferencing, and this reasoning capability in turn is used for knowledge-intensive learning. On the connectionist network level, the central process is the recruitment of new units and the assembly of units to represent new conceptual information. Free, uncommitted subnetworks are connected to the built-in knowledge network during learning. The goal of knowledge-intensive connectionist learning is to improve the operationality of the knowledge representation: mediated inferences, i.e., complex inferences which require several inference steps, are transformed into immediate inferences; in other words, recognition is based on the immediate excitation from features directly associated with a concept.
Bibliographic Information:
ICSI Technical Report TR-88-010
Bibliographic Reference:
J. Diederich. Knowledge-Intensive Recruitment Learning. ICSI Technical Report TR-88-010, November 1988